I am a Data Scientist bridging the gap between rigorous software engineering, advanced machine learning, and strategic business leadership. With a foundation in Computer Science and Software Engineering, a Master's in Computer Engineering (Data Science), and an MBA in Data Analytics, I architect end-to-end predictive pipelines that do more than just achieve high accuracy—they drive measurable business ROI.
- Production-Grade Architecture: My software engineering roots mean my machine learning pipelines are built to scale, deploy seamlessly, and remain maintainable in production environments.
- Algorithmic & Technical Depth: Advanced training in computer engineering and data science allows me to optimize complex models, engineer high-impact features, and build robust data systems.
- Strategic Execution: My MBA ensures that every model I develop—whether identifying critical employee turnover drivers or forecasting market trends—is explicitly aligned with executive priorities and bottom-line growth.
- Machine Learning & Analytics: Predictive Modeling, Logistic/Linear Regression, Ensemble Methods, Statistical Analysis, A/B Testing
- Engineering: Python, Scikit-learn, Pandas, End-to-End Pipeline Architecture, Version Control (Git)
- Business Intelligence: Data-Driven Strategy, ROI Optimization, Cross-functional Leadership, Stakeholder Communication
- HR Analytics: Employee Retention Model: Developed a logistic regression pipeline to identify key drivers of employee turnover, utilizing statistical odds ratios to provide actionable retention strategies for HR leadership.
- Real Estate Valuation: Predictive Modeling: Built a robust regression model to predict housing prices, featuring comprehensive exploratory data analysis and feature engineering.